An instruction set, or instruction set architecture (ISA), is the part of the computer architecture related to programming, and may include the native data types, instructions, register architecture, addressing modes, memory architecture, interrupt and exception handling, and external input and output (I/O). It should be noted that the term instruction generally refers herein to a macro-instruction—that is instructions that are provided to the processor for execution—as opposed to micro-instructions or micro-ops that result from a processor's decoder decoding macro-instructions).
The instruction set architecture is distinguished from the microarchitecture, which is the internal design of the processor implementing the ISA. Processors with different microarchitectures can share a common instruction set. For example, Intel Pentium 4 processors, Intel Core processors, and Advanced Micro Devices, Inc. of Sunnyvale Calif. processors implement nearly identical versions of the x86 instruction set (with some extensions having been added to newer versions), but have different internal designs. For example, the same register architecture of the ISA may be implemented in different ways in different microarchitectures using well known techniques, including dedicated physical registers, one or more dynamically allocated physical registers using a register renaming mechanism (e.g., the use of a Register Alias Table (RAT), a Reorder Buffer (ROB) and a retirement register file as described in U.S. Pat. No. 5,446,912; the use of multiple maps and a pool of registers as described in U.S. Pat. No. 5,207,132), etc. Unless otherwise specified, the phrases register architecture, register file, and register refer to that which is visible to the software/programmer and the manner in which instructions specify registers. Where specificity is desired, the adjective logical, architectural, or software visible will be used to indicate registers/files in the register architecture, while different adjectives will be used to designate registers in a given microarchitecture (e.g., physical register, reorder buffer, retirement register, register pool).
An instruction set includes one or more instruction formats. A given instruction format defines various fields (number of bits, location of bits) to specify, among other things, the operation to be performed and the operand(s) on which that operation is to be performed. A given instruction is expressed using a given instruction format and specifies the operation and the operands. An instruction stream is a specific sequence of instructions, where each instruction in the sequence is an occurrence of an instruction in an instruction format.
Scientific, financial, auto-vectorized general purpose, RMS (recognition, mining, and synthesis)/visual and multimedia applications (e.g., 2D/3D graphics, image processing, video compression/decompression, voice recognition algorithms and audio manipulation) often require the same operation to be performed on a large number of data items (referred to as “data parallelism”). Single Instruction Multiple Data (SIMD) refers to a type of instruction that causes a processor to perform the same operation on multiple data items. SIMD technology is especially suited to processors that can logically divide the bits in a register into a number of fixed-sized data elements, each of which represents a separate value. For example, the bits in a 64-bit register may be specified as a source operand to be operated on as four separate 16-bit data elements, each of which represents a separate 16-bit value. As another example, the bits in a 256-hit register may be specified as a source operand to be operated on as four separate 64-bit packed data elements (quad-word (Q) size data elements), eight separate 32-bit packed data elements (double word (D) size data elements), sixteen separate 16-bit packed data elements (word (W) size data elements), or thirty-two separate 8-bit data elements (byte (B) size data elements). This type of data is referred to as the packed data type or vector data type, and operands of this data type are referred to as packed data operands or vector operands. In other words, a packed data item or vector refers to a sequence of packed data elements; and a packed data operand or a vector operand is a source or destination operand of a SIMD instruction (also known as a packed data instruction or a vector instruction).
By way of example, one type of SIMD instruction specifies a single vector operation to be performed on two source vector operands in a vertical fashion to generate a destination vector operand (also referred to as a result vector operand) of the same size, with the same number of data elements, and in the same data element order. The data elements in the source vector operands are referred to as source data elements, while the data elements in the destination vector operand are referred to a destination or result data elements. These source vector operands are of the same size and contain data elements of the same width, and thus they contain the same number of data elements. The source data elements in the same bit positions in the two source vector operands form pairs of data elements (also referred to as corresponding data elements; that is, the data element in data element position 0 of each source operand correspond, the data element in data element position 1 of each source operand correspond, and so on). The operation specified by that SIMD instruction is performed separately on each of these pairs of source data elements to generate a matching number of result data elements, and thus each pair of source data elements has a corresponding result data element. Since the operation is vertical and since the result vector operand is the same size, has the same number of data elements, and the result data elements are stored in the same data element order as the source vector operands, the result data elements are in the same bit positions of the result vector operand as their corresponding pair of source data elements in the source vector operands. In addition to this exemplary type of SIMD instruction, there are a variety of other types of SIMD instructions (e.g., that have only one or has more than two source vector operands; that operate in a horizontal fashion; that generate a result vector operand that is of a different size, that have a different size of data elements, and/or that have a different data element order). It should be understood that the term destination vector operand (or destination operand) is defined as the direct result of performing the operation specified by an instruction, including the storage of that destination operand at a location (be it a register or at a memory address specified by that instruction) so that it may be accessed as a source operand by another instruction (by specification of that same location by the another instruction.
The SIMD technology, such as that employed by the Intel® Core™ processors having an instruction set including x86, MMX™, Streaming SIMD Extensions (SSE), SSE2, SSE3, SSE4.1, and SSE4.2 instructions, has enabled a significant improvement in application performance (Core™ and MMX™ are registered trademarks or trademarks of Intel Corporation of Santa Clara, Calif.). An additional set of SIMD extensions, referred to the Advanced Vector Extensions (AVX) (AVX1 and AVX2) and using the VEX coding scheme, has been released and/or published (e.g., see Intel® 64 and IA-32 Architectures Software Developers Manual, October 2011; and see Intel Advanced Vector Extensions Programming Reference, June 2011).
Many modern day processors are extending their capabilities to perform SIMD operations to address the continued need for vector floating-point performance in mainstream scientific and engineering numerical applications, visual processing, recognition, data-mining/synthesis, gaming, physics, cryptography and other areas of applications. Additionally, some processors are utilizing predication including the use of writemasks to perform operations on particular data elements of SIMD register.
SIMD architectures can only deliver maximum performance when executing vectorized code. However, a compiler is not always able to vectorize code. For example, when a loop has a lexically backward loop carried data dependency that cannot be resolved until run time, the compiler cannot vectorize the loop.